Thursday, 27 January 2011: 9:00 AM
307-308 (Washington State Convention Center)
The Regional Climate Centers produce a wide range of climate service products that are based on the station data available in the Applied Climate Information System (ACIS). Python is used to tie together a diverse web of components. ACIS data is stored in variety of formats and coordinated with metadata from different data stores. Communication between processes and Regional Centers must be efficient to synchronize and distribute the data. The dynamic nature of Python has allowed the system to evolve and utilize new technologies as they become available.
There has been increasing demand for the traditional climate products based on station data that has been interpolated to a grid. The existence of Python bindings for several statistical packages has enabled the Regional Climate Centers to address this need.
The choice and structure of the underlying data storage contribute to the performance of the system. We are able to compare several implementations of both data storage and caching.
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